Prior to setting forth a short discussion of the related art, it may be helpful to set forth definitions of certain terms that will be used hereinafter.
The term “MIMO” as used herein, is defined as the use of multiple antennas at both the transmitter and receiver to improve communication performance. MIMO offers significant increases in data throughput and link range without additional bandwidth or increased transmit power. It achieves this goal by spreading the transmit power over the antennas to achieve spatial multiplexing that improves the spectral efficiency (more bits per second per Hz of bandwidth) or to achieve a diversity gain that improves the link reliability (reduced fading), or increased antenna directivity.
The term “beamforming” sometimes referred to as “spatial filtering” as used herein, is a signal processing technique used in antenna arrays for directional signal transmission or reception. This is achieved by combining elements in the array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity.
The term “beamformer” as used herein refers to RF circuitry that implements beamforming and usually includes a combiner and may further include switches, controllable phase shifters, and in some cases amplifiers and/or attenuators.
The term “Receiving Radio Distribution Network” or “Rx RDN” or simply “RDN” as used herein is defined as a group of beamformers as set forth above.
The term “hybrid MIMO RDN” as used herein is defined as a MIMO system that employs two or more antennas per channel (N is the number of channels and M is the total number of antennas and M>N). This architecture employs a beamformer for each channel so that two or more antennas are combined for each radio circuit that is connected to each one of the channels.
In hybrid MIMO RDN receiving systems, when the phases of the received signals from each antenna are properly adjusted or tuned with respect to one another, the individual signals may be combined and may result in an improved SNR or data throughput for the receiving system.
One tuning phase method is based on channel estimation of each antenna which contributes to the beamforming; the invention here is using a different method for identifying best-phase alignments for beamforming purposes; it is based on modifying phases iteratively while monitoring their combined signal quality.
When more than two antennas are involved, the number of iteration increases, thus longer periods of quasi-static fading are needed for stable process, as well as mechanism to address cases where quasi-static fading ceases to exist.
For example, in Cellular protocols, quality indicators are typically repeated ˜1000-2000 times per second. In WiFi protocols, they may have lower repetition rates, depending on traffic and number of users. In Mobile environment, fading change rate may vary between ˜10 times a second (static environment) and 100-200 times a second (vehicular), although it can be as fast as 1000-2000 times per second.
Consequently, when multiple antennas beamforming is based on an iterative process, it has to strike a balance between using the maximum number of available antennas, and the need to update each one of them fast enough to trace the fading variations.
As discussed above, various methods are known in the art for tuning of multiple-antenna beamformers. Each method has its advantages and disadvantages. One method is based on making a direct measurement of the antennas' signals phases & amplitudes and calculates corresponding corrections (can be carried out via channel estimation). Another method includes trying out various possible solutions and grading them per their impact on various quality indicators. This can be carried out via blind search of the best set of phases where there is a systematic gradient seeking method, or via blind scan where there is preference to try each and every possible phase value, or some other method where trial and error are the driver of the tuning process. All of these trial and error methods, including blind scan and blind search, are referred herein as “blind beamforming tuning algorithms” or simply: “blind algorithms”.
It is generally agreed that while channel estimation based method is the faster tuning method, it is not always the preferred one. For instance, in some cases, channel estimation requires digging info that may not be provided over standard signals coming out of baseband processors, while quality indicators needed for blind search may be readily available. Another consideration relates to dealing with interference—where co-channel undesired signals dominate, and when the receiver does not allocate resources for interference cancellation, then blind scan may yield better results (e.g., maximize the overall data rates).